In compressive sensing we are dealing with a reduced set of signal observations [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}. The reduced set of observations can be caused by a desire to acquire a signal with a low number of observations or by
physical unavailability to measure the signal at all possible sampling positions and to get a complete set of samples [4]}, [5]}. In some applications, signal samples may be heavily
corrupted at some arbitrary positions that their omission could be the best approach to their processing, when we are left with a reduced set of signal samples to reconstruct the signal [14]}, [15]}, [16]}. The main condition to fully reconstruct the signal from a reduced set of observations is the signal sparsity in a transformation domain. Sparse signals can be reconstructed from reduced measurements under some conditions [6]}, [18]}, [19]}, [20]}. Applications of
compressive sensing methods are numerous, including radar signal processing [21]}, [22]}, time-frequency analysis [23]}, [24]}, [25]}, data hiding [26]}, wireless communications [27]}, and image processing [28]}.
